• Weird Whales: collection of 3350 whales programmatically generated, each with their unique characteristics and traits. Created by a 12-year-old programmer named Benyamin Ahmed who made the buzz.
• EtherScan: block explorer to view information about transactions, verify contract code, visualize network data \(\rightarrow\) access to more raw data
• Analysis done in October 2021 and updated in December 2022: Link

resEventTransfer <- GET("https://api.etherscan.io/api",
                          query = list(module = "logs", 
                                       action = "getLogs", 
                                       fromBlock = fromBlock, 
                                       toBlock = "latest",
                                       address = "0x96ed81c7f4406eff359e27bff6325dc3c9e042bd", 
                                       topic0 = "0xddf252ad1be2c89b69c2b068fc378daa952ba7f163c4a11628f55a4df523b3ef",
                                       apikey = EtherScanAPIToken)) 

Where is the sales price? On OpenSea, sales are managed by the main contract and if approved, the second contract is called (here Weird Whales), which then triggers the transfer \(\rightarrow\) need to download all the transactions from the OpenSea main smart contract address and then filter for the ones related to Weird Whales (~ 10000 API calls, can take several hours…).

• Ethereum / USD rate is highly volatile, need to obtain the historical ETH market price if we want to convert ETH to USD.
• A spline is fitted on data from the CoinGecko API

• High variability in prices at the begining (buzz), followed by a quieter period.
• Some NFTs are exchanged outside the OpenSea Market (price = 0) \(\rightarrow\) difficult to trace….
• Starting March 2022, all sales price felt to 0 \(\rightarrow\) OpenSea was hacked in March, they probably updated their smart contract…

• Perfect data to be plotted as a network. We will display all the wallet addresses that have ever traded Weird Whales (restricted to tokens involved in at least 10 transactions).

• Networks are described by:

  • Vertices (or nodes): the wallet addresses
  • Edges (or links): the transactions

• network package:

# create the network using network function
network <- network(edges,  
                   vertex.attr = vertices, 
                   matrix.type = "edgelist", 
                   loops = T, 
                   multiple = T, 
                   ignore.eval = F)

About 2/3 of the transactions happened very shortly after the NFT’s creation

• Made with the network and networkDynamic packages

• H3: geospatial indexing system using a hierarchical hexagonal grid. At resolution 8: earth is covered by 691,776,122 hexagons \(\rightarrow\) we convert to lat/lng

• We start by creating an empty world map on which we overlay the hotspot data.
• Plotting all the individual hotspots on a map would be too much (> 500k hotspots) \(\rightarrow\) we cluster the hotspots into hexagons (hexbin and geom_hex)
• Most hotspots are located in North America, Europe and Asia, mostly in big cities. Practically no hotspots in Africa, Russia and very few in South America. A few hotspots in the middle of the ocean… Data issue or cheating to increase rewards?

• We understood that hotspots are distributed on the planet and provide good coverage in big cities. Are they being actively used by connected devices and how often? \(\rightarrow\) Transaction data
• Every 24 bytes sent cost 1 Data Credit (DC) = $0.00001
• We can look at network usage from two perspectives: (1) check the volume of data exchanged and (2) check how often the hotspots have been involved in data transfer with connected devices

• Slow linear increase followed by an exponential increase, which is finally followed by a fast linear increase
• Glitch in November 2021: major outage of the blockchain
• Despite having about 15% of the hotspots, Asia don’t seem to be so active in terms of data transfer
• Median number of transactions per hotspot: 42
• 40.48% hotspots did not participate in any transaction so far.

• Transactions mainly occur in North America before mid 2020, then followed by a strong wave in Europe and Asia. Barely no transaction have occurred in South America and Africa.
• Made with the gganimate package

• US: Transactions are homogeneously distributed across the country although the peaks of activity (note that the legend is logarithmic!) are located around big cities (New York, Los Angeles, San Francisco, Miami)
• BE: Transactions are not homogeneously distributed across the country \(\rightarrow\) most transactions happen in the upper part of the country, which is consistent with the lower part of the country being scarcely populated (Ardennes)
• Made with the rayshader package, interactive version available in the original article